A Computational Model of Linguistic Humor in Puns

Humor plays an essential role in human interactions. Precisely what makes something funny, however, remains elusive. While research on natural language understanding has made significant advancements in recent years, there has been little direct integration of humor research with computational model...

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Bibliographic Details
Published in:Cognitive science 2016-07, Vol.40 (5), p.1270-1285
Main Authors: Kao, Justine T., Levy, Roger, Goodman, Noah D.
Format: Article
Language:English
Subjects:
Ambiguity
Brief Report
Brief Reports
Cognition & reasoning
Cognitive Processes
Comprehension
Computational modeling
Correlation
Humans
Humor
Interaction
Language
Language processing
Language Usage
Linguistics
Mathematical Models
Noisy channel
Sentences
Theories
Wit and Humor as Topic
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Summary:Humor plays an essential role in human interactions. Precisely what makes something funny, however, remains elusive. While research on natural language understanding has made significant advancements in recent years, there has been little direct integration of humor research with computational models of language understanding. In this paper, we propose two information‐theoretic measures—ambiguity and distinctiveness—derived from a simple model of sentence processing. We test these measures on a set of puns and regular sentences and show that they correlate significantly with human judgments of funniness. Moreover, within a set of puns, the distinctiveness measure distinguishes exceptionally funny puns from mediocre ones. Our work is the first, to our knowledge, to integrate a computational model of general language understanding and humor theory to quantitatively predict humor at a fine‐grained level. We present it as an example of a framework for applying models of language processing to understand higher level linguistic and cognitive phenomena.
ISSN:0364-0213
1551-6709
DOI:10.1111/cogs.12269